Data underlying the research of Shared Micromobility, Shared by Everyone?
收藏4TU.ResearchData2023-04-14 更新2026-04-23 收录
下载链接:
https://data.4tu.nl/datasets/131bb791-9e8d-4e7b-bf59-f30026c6639c/1
下载链接
链接失效反馈官方服务:
资源简介:
Previously, little research had been done about the non-users of shared micromobility. An exploratory research was conducted to identify reasons why individuals did not use shared micromobility, such as shared mopeds and bikes. During this research, a survey was carried out to collect data about the non-users.<br>The survey was conducted in four neighborhoods: Bijlmer Centrum, Museumkwartier, Volewijk, and Houthavens in Amsterdam. To collect a diverse range of socio-demographic profiles, people were approached in public spaces using a face-to-face interviewing technique. In a short survey, respondents were asked a maximum of 15 questions. A total of 50 people were surveyed with an equal distribution across the four locations.<br>The first section consisted of open questions that were asked to the respondents about the (non) usage of shared micromobility. With the respondent's permission, the survey was audio recorded so that the answers could be transcribed and anonymized afterward. The second section consisted of multiple-choice and closed-ended questions designed to identify various non-user groups.<br><br>The dataset features the results of a thematic analysis conducted on the responses collected from non-users of shared micromobility. This analysis categorizes the respondents' phrases into codes, sub-themes and themes, resulting in a clearer and more transparent analysis process.
此前,鲜有针对共享微出行(shared micromobility)非使用者的相关研究。本研究为探索性研究,旨在探究个体不使用共享微出行的原因,例如共享电动轻便摩托与共享自行车。本次研究通过问卷调查收集非使用者的相关数据。
本次调研覆盖阿姆斯特丹的四个街区:比耶尔默中心(Bijlmer Centrum)、博物馆区(Museumkwartier)、福利维克(Volewijk)与豪特哈文斯(Houthavens)。为采集多样化的社会人口统计学特征样本,研究人员在公共空间采用面对面访谈法招募受访者。本次简短问卷最多设置15个问题,最终共完成50份有效问卷,四个街区的受访者分布均衡。
问卷第一部分为开放式问题,用于询问受访者有关共享微出行的使用与非使用情况。经受访者同意,调研过程进行了录音,以便后续将语音转录为文本并开展匿名化处理。问卷第二部分包含多项选择题与封闭式问题,旨在划分不同类型的非使用者群体。
本数据集收录了针对共享微出行非使用者调研反馈的主题分析结果。该分析将受访者的表述编码为编码项、子主题与主题,使得分析过程更为清晰透明。
创建时间:
2023-04-14



